import pandas as pd
import plotly.express as px


pd.options.display.max_rows=50

# Load the Utah 1880 Census data
utah_df = pd.read_csv('utah-census-1880.csv',encoding='utf-8')

county_counts= utah_df['county'].value_counts()

# Apply reset_index() to convert the index (county names) into a regular column
county_counts = county_counts.reset_index()
county_counts.columns = ['county', 'count']

# Display the counts after reset_index
county_counts

	county	count
0	SALT LAKE	31989
1	UTAH	17967
2	CACHE	12562
3	WEBER	12358
4	SANPETE	11538
5	BOX ELDER	6766
6	DAVIS	5166
7	SUMMIT	4921
8	TOOELE	4496
9	SEVIER	4457
10	WASHINGTON	4140
11	IRON	4034
12	BEAVER	3864
13	MILLARD	3719
14	JUAB	3477
15	KANE	3090
16	WASATCH	2928
17	MORGAN	1783
18	PIUTE	1616
19	UINTAH	799
20	EMERY	556
21	RICH	281
22	SAN JUAN	204

# Create a simple bar chart
fig = px.bar(county_counts, x='county', y='count')

# Show the figure
fig.show()

# Add title and customize axis labels
fig = px.bar(
    county_counts,
    x='county',
    y='count',
    title='Population by County in Utah (1880)',  # Add a title
    labels={'county': 'County', 'count': 'Population'}  # Rename axis labels
)

# Display the chart
fig.show()

# Add color and use a cleaner template
fig = px.bar(
    county_counts,
    x='county',
    y='count',
    title='Population by County in Utah (1880)',
    labels={'county': 'County', 'count': 'Population'},
    color='count',                     # Color bars by population
    color_continuous_scale='Inferno_r',  # Use a reversed color scale
    template='plotly_white'            # Use a clean white template
)

# Display the chart
fig.show()

# Add color and use a cleaner template
fig = px.bar(
    county_counts,
    x='county',
    y='count',
    title='Population by County in Utah (1880)',
    labels={'county': 'County', 'count': 'Population'},
    color='count',                     # Color bars by population
    color_continuous_scale='Inferno_r',  # Use a reversed color scale and the Inferno color scheme
    template='plotly_white'            # Use a clean white template
)

# Update layout with additional customizations
fig.update_layout(
    xaxis_title='County',                  # Customize x-axis title
    yaxis_title='Number of People',        # Customize y-axis title
    xaxis_tickangle=-45,                   # Rotate x-axis labels 45 degrees
    height=500,                            # Set chart height in pixels
    width=800,                             # Set chart width in pixels
    title_font=dict(size=22),              # Change title font size
    plot_bgcolor='white',                   # Set plot background color
    margin=dict(l=40, r=40, t=80, b=80),   # Adjust margins (left, right, top, bottom)
    showlegend=True,                       # Show the color scale legend
    legend_title_text='Population',        # Set legend title
    hoverlabel=dict(                       # Customize hover label appearance
        bgcolor="white",
        font_size=12,
        font_family="Georgia")
)

# Display the chart
fig.show()

# First, let's prepare our data
# Count the frequency of each occupation, take the top 10, and reset the index
top_occupations = utah_df['occupation'].value_counts().head(10).reset_index()
top_occupations.columns = ['occupation', 'count']

# Display the prepared data
top_occupations

	occupation	count
0	KEEPING HOUSE	23031
1	AT HOME	13743
2	FARMER	9535
3	LABORER	6846
4	AT SCHOOL	6549
5	MINER	2596
6	WORKS ON FARM	2026
7	CARPENTER	1225
8	SERVANT	1219
9	FARM LABORER	1065

chart= px.bar(top_occupations,
              x='occupation',
              y='count',
              title= 'Top 10 Occupations in Utah (1880)',
              labels={'occupation': 'Job', 'count': 'Number of People'},
              color='count',
              color_continuous_scale='Agsunset',
              template='xgridoff',
             )




chart.update_layout(
    xaxis_title='Job',                  # Customize x-axis title
    yaxis_title='Number of People',        # Customize y-axis title
    xaxis_tickangle=-45,                   # Rotate x-axis labels 45 degrees
    height=400,                            # Set chart height in pixels
    width=800,                             # Set chart width in pixels
    title_font=dict(size=30),              # Change title font size
    plot_bgcolor='white',                   # Set plot background color
    margin=dict(l=80, r=40, t=80, b=100),   # Adjust margins (left, right, top, bottom)
    showlegend=True,                       # Show the color scale legend
    legend_title_text='Number of People',        # Set legend title
    hoverlabel=dict(                       # Customize hover label appearance
        bgcolor="white",
        font_size=12,
        font_family="Papyrus")
)

chart.show()